Grok 3 vs Llama 3.3 70B Instruct

Across our 12-test benchmark suite, Grok 3 outscores Llama 3.3 70B Instruct on 6 tests and ties on 6. Llama 3.3 70B Instruct wins none.

Where Grok 3 leads:

• Strategic analysis (5 vs 3): Grok 3 ties for 1st among 54 models; Llama 3.3 70B ranks 36th. This tests nuanced tradeoff reasoning with real numbers — a meaningful gap for business analysis and research tasks.
• Faithfulness (5 vs 4): Grok 3 ties for 1st among 55 models; Llama 3.3 70B ranks 34th. For RAG and summarization workflows where hallucination is a liability, this two-point spread matters.
• Persona consistency (5 vs 3): Grok 3 ties for 1st among 53 models; Llama 3.3 70B ranks 45th — near the bottom. If you're building character-driven products or assistants that need to hold a voice across a long session, Llama 3.3 70B struggles here.
• Agentic planning (5 vs 3): Grok 3 ties for 1st among 54 models (with 14 others); Llama 3.3 70B ranks 42nd. Goal decomposition and failure recovery are core to autonomous agent frameworks — Grok 3 is substantially stronger on this dimension in our testing.
• Multilingual (5 vs 4): Grok 3 ties for 1st among 55 models; Llama 3.3 70B ranks 36th. For non-English deployments, Grok 3 has a consistent edge.
• Structured output (5 vs 4): Grok 3 ties for 1st among 54 models; Llama 3.3 70B ranks 26th. JSON schema compliance and format adherence are foundational for API integrations — Grok 3 is more reliable here.

Where the models tie:

• Tool calling (4/4): Both rank 18th of 54, sharing the score with 29 models. Equivalent function selection and argument accuracy in our tests.
• Classification (4/4): Both tied for 1st of 53, sharing the score with 29 others. Solid and identical performance.
• Long context (5/5): Both tied for 1st of 55 — retrieval accuracy at 30K+ tokens is equivalent.
• Safety calibration (2/2): Both rank 12th of 55. Neither model stands out on refusing harmful requests while permitting legitimate ones.
• Constrained rewriting (3/3): Both rank 31st of 53. Neither excels at compression within hard character limits.
• Creative problem solving (3/3): Both rank 30th of 54. Equivalent and middling performance on generating non-obvious, feasible ideas.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has third-party data available: it scores 41.6% on MATH Level 5 and 5.1% on AIME 2025. Both place it last among all models scored on those tests (14th of 14 and 23rd of 23, respectively), indicating limited performance on advanced math. No external benchmark scores are available for Grok 3 in this dataset.

The price gap here is stark: Grok 3 costs $3.00/M input and $15.00/M output tokens, while Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output tokens — a 46.9x ratio on outputs. At 1M output tokens/month, that's $15 vs $0.32. At 10M tokens, $150 vs $3.20. At 100M tokens — a realistic volume for a production API integration — you're looking at $1,500 vs $32 per month. For applications where Grok 3's advantages in strategic analysis, faithfulness, and agentic planning directly drive business value (e.g., document intelligence, multi-step agents, RAG pipelines), that premium may pay for itself quickly. For bulk classification, structured data extraction, or long-context retrieval where both models score identically in our testing, Llama 3.3 70B Instruct is the rational default. Developers running high-throughput inference at scale should treat the cost delta as a first-order decision variable.

Choose Grok 3 if: You're building agentic workflows, RAG pipelines, or multi-step automation where faithfulness, agentic planning, and structured output quality directly affect reliability. Also choose it for multilingual products, enterprise document intelligence, or any application where strategic analysis or persona consistency are core requirements — and where the $15/M output token cost is justifiable against the quality gains.

Choose Llama 3.3 70B Instruct if: Your workload is dominated by classification, long-context retrieval, or tool calling — where both models score identically in our tests — and you're operating at volumes where the 46.9x cost difference matters. At 100M output tokens/month, you save roughly $1,468. It's also the rational choice for developers who need to self-host or want to avoid vendor lock-in to a proprietary model. For straightforward structured extraction or routing tasks, Llama 3.3 70B Instruct delivers equivalent results at a fraction of the price.